Retocagem digital

A correção e edição de imagem e de vídeo têm vindo a proliferar. Neste tipo de atividades, utiliza-se normalmente uma técnica que tem tido uma aceitação cada vez mais crescente com o passar do tempo, a técnica do Retocagem Digital. O primeiro algoritmo matemático capaz de traduzir o trabalho humano em trabalho computacional, surgiu em 2000 através de Bertalmio e Sapiro. De notar é que, na correção de imagem e de vídeo, o principal foco desta técnica é essencialmente corrigir pequenos “erros”, os quais são detetáveis ao olho humano leia-se “olho nu” numa imagem. Alguns exemplos podem ser enumerados, desde riscos, ou pontos de poeira, até a remoção de outros objetos como os logotipos, marcas de água (watermarks), pessoas, etc. Sendo assim, o objetivo principal tornar essas modificações impercetíveis na imagem. Posto isto, consideraram-se assim diferentes técnicas que são usadas para corrigir os diferentes problemas supracitados. Estas técnicas podem englobar uma parte mais pequena da imagem na qual se tem de ter em conta apenas a dispersão de cores numa matriz bastante povoada e dispersa, ou na parte maior da imagem, na qual se tem em conta essencialmente quais são os pixéis vizinhos ou até mesmo as cores desses pixéis, de modo a manter a coerência no preenchimento de cada pixel, considerando uma dada área.Digital image and video correction and editing have been proliferating. This kind of activities rely on techniques, usually know as Digital Inpainting or Digital Retouching, that have been increasingly growing in popularity as well as acceptance in the course of time. Digital Retouching techniques are relatively recent, becoming popular since the year 2000, and are nowadays widely used in several areas. In image and video correction, the main objective of this technique is essentially to correct "imperfections" that are detectable by the human eye, known as "naked eye". This "imperfections" correction may consist of image scratches or dust spots removal, up to the removal of larger objects such as logos, watermarks, people, etc. Nevertheless, the main goal of Digital Retouching is to make these changes imperceptible to the human viewer or as natural as possible. This Dissertation considers different techniques that are used to correct the several problems. It was developed a Digital Retouching application to evaluate the performance of various techniques mentioned above through image quality metrics

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

SPATIO-TEMPORAL TEXTURE SYNTHESIS AND IMAGE INPAINTING FOR VIDEO APPLICATIONS

In this paper we investigate the application of texture synthesis and image inpainting techniques for video applications. Working in the non-parametric framework, we use 3D patches for matching and copying. This ensures temporal continuity to some extent which is not possible to obtain by working with individual frames. Since, in present application, patches might contain arbitrary shaped and multiple disconnected holes, fast fourier transform (FFT) and summed area table based sum of squared difference (SSD) calculation [1] cannot be used. We propose a modification of above scheme which allows its use in present application. This results in significant gain of efficiency since search space is typically huge for video applications. 1